Neuromorphic networks or physical neural networks are used in pattern recognition and classification, with many potential applications from fingerprint, iris, and face recognition to target acquisition, etc. The parameters (e.g., ‘synaptic weights’) of the neuromorphic networks are adaptively trained on a set of patterns during a learning process, following which the neuromorphic network is able to recognize or classify patterns of the same kind.
A key component of a neuromorphic network is the ‘synapse,’ at which weight information is stored, typically as a continuous-valued variable. For applications that would benefit from compact, high-performance, low-power, portable neuromorphic network computation, it is desirable to be able to construct high-density hardware neuromorphic networks having a large number of synapses (109-1010 or more). Currently a neuromorphic network is typically realized as a software algorithm implemented on a general-purpose computer, although hardware for neuromorphic networks exist.
Currently, physical neural networks may be implemented with adjustable resistance material used to emulate the function of a neural synapse. The coupling strength between an input and an output “neuron” may be adjusted through the magnitude of the resistance of the synapse. A timing-dependent coupling between nodes within a network, with a synapse located therebetween, may be advantageous. The synapse would then provide a time delay to an incoming pulse before it is transmitted to the output. Implementations of time delay elements may be mercury delay lines. The time-delay that is achieved by such a mercury delay line is determined by the length of the tube divided by the speed of sound in liquid mercury. Thus, when an acoustic signal is transmitted through the mercury delay line, the signal delivery from one end to the other may be impacted. To tune such a configuration, the length of the mercury delay line may be changed.
Neuromorphic network applications may include pattern recognition, classification, and identification of fingerprints, faces, voiceprints, similar portions of text, similar strings of genetic code, etc.; data compression; prediction of the behavior of systems; feedback control; estimation of missing data; “cleaning” of noisy data; and function approximation or “curve fitting” in high-dimensional spaces. Moreover, finding local or global minima (maxima) of complex optimization problems may be targeted by neuromorphic networks.